Method and system for predicting service life of PLA-based composite mulching film based on p-norm algorithm and storage medium

Abstract

The invention relates to a method and system for predicting the service life of a PLA-based composite mulching film based on a p-norm algorithm and a storage medium. According to the technical scheme, the preparation method comprises the following steps: grafting modified cellulose nanocrystals with citric acid, and compounding the modified cellulose nanocrystals with polylactic acid to prepare a film; respectively carrying out degradation experiments of indoor ultraviolet irradiation and outdoor planting environment; according to the method, an ultraviolet degradation prediction method based on a p-norm algorithm is constructed, change rules of indexes such as elongation at break, tensile strength, a light transmission coefficient and a reflection coefficient are analyzed by associating degradation sequences of indoor ultraviolet irradiation and an outdoor planting environment, and a quantitative relation model of degradation days and performance parameters is established. The high-degradation composite film is combined, the limitation that degradation of a traditional mulching film is uncontrollable is broken through, the crop growth cycle is dynamically matched through the prediction model, and the degradation process is precisely regulated and controlled. Wherein the determination coefficient R2 of the tensile strength prediction model is equal to 0.996, the actual application error is lower than 5%, and intelligent adaptation of the degradation rate and agricultural requirements is achieved.

Description

Technical Field

[0001] It belongs to the technical field of agricultural materials, and specifically relates to a service life prediction model of a PLA-based composite plastic film based on the p-norm algorithm, its establishment method and application Background Art

[0002] Traditional plastic mulch films are widely used in agricultural production due to their functions such as moisture preservation and weed suppression. However, their non-degradable characteristics lead to a large amount of plastic debris remaining in the soil, forming long-term cumulative "white pollution" and causing serious ecological problems such as soil compaction and microbial community imbalance. To solve this problem, biodegradable materials such as polylactic acid (PLA) are regarded as ideal alternatives due to their biodegradable characteristics. However, their slow degradation rate and lack of accurate life prediction methods limit their large-scale popularization and application

[0003] The patent document with the publication number of CN119517255A discloses a degradation evaluation system based on the epoxy alcoholysis model, which relies on the time-temperature superposition principle and requires multi-temperature accelerated experiments and complex calculations combined with the Arrhenius equation. Although this method can predict the degradation behavior of materials to a certain extent, it has high implementation costs, takes a long time, and is difficult to accurately reflect the complexity and dynamics of the real field environment

[0004] The ultraviolet-induced degradation of PLA mainly causes chain scission through the Norrish II process. The article "Life cycledesign of fullybio-based poly(lactic acid)composites with high flame retardancy,UVresistance,and degradation capacity" published by Shuang Qiu et al. in the journal "Journal of Cleaner Production" pointed out that its degradation rate is closely related to the light intensity and duration

[0005] Based on this, the present invention proposes a prediction model based on the p-norm algorithm. By analyzing the correlation between laboratory accelerated ultraviolet degradation (UID) and outdoor planting environment degradation (PED), and using key performance indicators (such as tensile strength, light transmittance coefficient), the rapid and accurate prediction of the service life of agricultural plastic films is realized, significantly simplifying the test process and improving the prediction accuracy Summary of the Invention

[0006] Based on the above-mentioned disadvantages and deficiencies existing in the prior art, the first object of the present invention is to provide a method for predicting the service life of a PLA-based composite plastic film based on the p-norm algorithm

[0007] A method for predicting the service life of PLA-based composite plastic films based on the p-norm algorithm, comprising the following steps:

[0008] (1) Using the p-norm algorithm, analyze the time series data of the performance indicators of the polylactic acid composite material under ultraviolet irradiation degradation (UID) and planting environment degradation (PED) conditions, and evaluate their similarity;

[0009] (2) Based on the similarity analysis in step (1), establish an association model between the number of UID days and the number of PED days through a two-step method, specifically:

[0010] (i) Fit the non-linear regression curve of the number of UID treatment days (x1) and the change in performance indicators (y1) to obtain the first function (F1);

[0011] (ii) According to F1, reverse-infer the assumed number of UID days (x2), combine the linear regression curve of the number of PED days (x3) and the assumed number of UID days (x2), fit the second function (F2), and finally calculate the predicted number of PED days (x4) through F2.

[0012] As a preferred solution, the performance indicators include elongation at break, tensile strength, light transmittance coefficient, and reflection coefficient.

[0013] As a preferred solution, the UID experimental conditions include: the ultraviolet wavelength is UVA-340, the irradiation energy is 0.5-1.5 W·m-2, the temperature is 15-40 °C, each cycle lasts for 1-3 days, and the total number of experimental cycles is 10-15 times.

[0014] As a preferred solution, the polylactic acid composite material is a PLA-CCNC composite film, and its preparation method includes: graft-modifying cellulose nanocrystals (CNC) with citric acid, blending them with polylactic acid (PLA) at a mass ratio of 3%, and preparing a film by solution casting.

[0015] As a preferred solution, the data acquisition interval of the performance indicators is once every 10-15 days, and the specification of the soil-covered film in the PED experiment is 5 cm × 5 cm.

[0016] As a preferred solution, the p-norm algorithm is used to align the degradation data of different time scales and calculate the 2-norm of the UID and PED degradation processes to determine their correlation.

[0017] As a preferred solution, the life prediction method further includes a pretreatment step for the polylactic acid composite material sample, including ultrasonic cleaning, rinsing with deionized water, and drying at room temperature.

[0018] The second object of the present invention is to provide a prediction system according to the method described in any one of the above, including:

[0019] (i) A data acquisition module for obtaining performance index data in UID and PED experiments;

[0020] (ii) A p - norm algorithm processing module for analyzing the similarity of time - series data;

[0021] (iii) A model construction module for generating F1 and F2 functions and outputting the predicted PED days.

[0022] As a preferred solution, the system is integrated into a computer or a cloud platform, supporting real - time data input and dynamic model update.

[0023] The third object of the present invention is to provide a storage medium containing computer - executable instructions, and when the instructions are executed by a processor, the steps of any of the above - mentioned methods are implemented.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] (1) Through the p - norm algorithm, the present invention can simulate long - term outdoor degradation (such as 60 days) with only a short - term indoor ultraviolet acceleration experiment (such as 30 days), and the experimental period is shortened by more than 50%, greatly improving the prediction efficiency.

[0026] (2) The present invention uses the p - norm algorithm to dynamically align the ultraviolet degradation (UID) and planting degradation (PED) data, eliminating the interference of time offset, and the prediction accuracy of the tensile strength reaches R 2 = 0.996, and the error rate ≤ 3.36%, which is significantly better than the traditional linear regression model.

[0027] (3) By adding 3wt% citric acid - grafted cellulose nanocrystals (CCNC), the tensile strength and water vapor barrier property of the composite film are significantly improved compared with pure PLA mulch film. At the same time, by regulating the CCNC content, the degradation rate is accurately matched to achieve the dual optimization of "strong performance and accurate degradation".

[0028] (4) By establishing a dynamic mapping relationship between ultraviolet intensity and degradation rate, the present invention can be adapted to different regions (such as shortening the prediction period in high - ultraviolet regions), guiding farmers to select mulch film parameters according to crop needs, and covering a wider range of scenarios.

[0029] (5) The existing non - degradable mulch film has a high cleaning cost, while the traditional degradable film is prone to premature failure due to mismatched lifetimes. The present invention can reduce the mulch film residue pollution by more than 50% through accurate prediction of the degradation period, and at the same time optimize the crop growth environment, reduce the cost of farmers, and promote the green transformation of agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1It is a graph showing the change in the quality of environmental degradation of the PLA-CCNC composite films of Examples 1-4 and Comparative Example 1 of the present invention, and the pure PLA film of Comparative Example 2;

[0031] Figure 2 It is a graph showing the results of the service life prediction model of the PLA-based composite mulch film based on the p-norm algorithm in Examples 1-4 of the present invention;

[0032] Figure 3 It is a graph showing the results of the service life prediction model of the PLA-based composite mulch film based on the linear regression equation algorithm in Comparative Example 1 of the present invention;

[0033] Figure 4 It is a graph showing the results of the service life prediction model of the pure PLA mulch film based on the p-norm algorithm in Comparative Example 2 of the present invention. Detailed implementation manners

[0034] In order to make the technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0035] Example 1:

[0036] The method for establishing a service life prediction model of the PLA-based composite mulch film based on the p-norm algorithm in this example includes:

[0037] Mix 1 g of MCC with 60 mL of 6 mol / L HCl solution, ultrasonicate for 10 minutes and stir and react at 90 °C for 4 hours. After cooling, wash with water until neutral, and obtain CNC through centrifugation, freeze-drying and grinding. Then mix the obtained CNC powder with 5% (w / w) citric acid (CA) and 1% (w / w) sodium hypophosphite (SHP). In the mixed solution, CNC:CA:H2O:SHP = 5:15:285:2.88 (w / w). After reacting for 1 hour, transfer it to a vacuum oven at 25 °C and 0.6 bar for 2 hours, take it out and let it stand at room temperature for 12 hours. Then heat the suspension at 60 °C for 48 hours to remove water, and then heat it at 130 °C for 4 hours. Finally, obtain the CCNC powder through washing, centrifugation, dialysis purification and freeze-drying and grinding.

[0038] Dissolve 0.97 g of PLA particles in 9 mL of chloroform, stir at room temperature until dissolved, add 0.03 g of CCNC, ultrasonicate and stir for 24 h, and finally prepare a composite film by the solution casting method, and fully dry it at room temperature in a fume hood to obtain a PLA-CCNC composite film.

[0039] Cover the plant planting pots with the PLA-CCNC composite film. Take samples every 15 days, ultrasonically clean them, and mark them as PED samples after natural drying. Place the PLA-CCNC composite film in a multi-lamp accelerated aging chamber (UVA-340, incident energy 1.1 W·m-2), and conduct ultraviolet irradiation at 35 °C. Take samples every 72 hours and mark them as UID samples.

[0040] Use a universal testing machine (Instron 5943) to test the tensile strength of the film at room temperature. Each sample is tested 5 times, and the average value is taken to reduce errors.

[0041] Use the p-norm algorithm to analyze the correlation between the tensile strength and the degradation days (PED and UID). By fitting the non-linear relationship curve of the tensile strength and the UID days, establish the prediction model F1. According to the F1 model, calculate the hypothetical UID days, combine the PED days to fit the linear relationship curve F2, and accurately estimate the service life of the PLA-CCNC composite film in the outdoor planting environment through the F2 model.

[0042] Example 2:

[0043] The method for establishing a service life prediction model of a PLA-based composite mulch film based on the p-norm algorithm in this example includes:

[0044] Mix 1 g of MCC with 60 mL of 6 mol / L HCl solution, ultrasonically treat for 10 minutes and stir and react at 90 °C for 4 hours. After cooling, wash with water until neutral, and obtain CNC through centrifugation, freeze-drying and grinding. Then mix the obtained CNC powder with 5% (w / w) citric acid (CA) and 1% (w / w) sodium hypophosphite (SHP). In the mixed solution, CNC:CA:H2O:SHP = 5:15:285:2.88 (w / w). After reacting for 1 hour, transfer it to a vacuum oven at 25 °C and 0.6 bar for 2 hours, take it out and let it stand at room temperature for 12 hours. Then heat the suspension at 60 °C for 48 hours to remove moisture, and then heat it at 130 °C for 4 hours. Finally, obtain the CCNC powder through washing, centrifugation, dialysis purification and freeze-drying and grinding.

[0045] Dissolve 0.97 g of PLA particles in 9 mL of chloroform, stir at room temperature until dissolved, add 0.03 g of CCNC, ultrasonically treat and stir for 24 h, and finally prepare the composite film by the solution casting method. After fully drying at room temperature in a fume hood, the PLA-CCNC composite film can be obtained.

[0046] Cover the plant planting pots with the PLA-CCNC composite film. Take samples every 15 days, ultrasonically clean them, and mark them as PED samples after natural drying. Place the PLA-CCNC composite film in a multi-lamp accelerated aging chamber (UVA-340, incident energy 1.1 W·m-2), and conduct ultraviolet irradiation at 35 °C. Take samples every 72 hours and mark them as UID samples.

[0047] Use a universal testing machine (Instron 5943) to test the elongation at break of the film at room temperature. Each sample is tested 5 times, and the average value is taken to reduce errors.

[0048] Use the p-norm algorithm to analyze the correlation between the elongation at break and the degradation days (PED and UID). By fitting the non-linear relationship curve of the elongation at break and the UID days, establish the prediction model F1. According to the F1 model, calculate the hypothetical UID days, combine the PED days to fit the linear relationship curve F2, and accurately estimate the service life of the PLA-CCNC composite film in the outdoor planting environment through the F2 model.

[0049] Example 3:

[0050] The method for establishing a service life prediction model of a PLA-based composite mulch film based on the p-norm algorithm in this example includes:

[0051] Mix 1 g of MCC with 60 mL of 6 mol / L HCl solution, ultrasonically treat for 10 minutes and stir and react at 90 °C for 4 hours. After cooling, wash with water until neutral, and obtain CNC through centrifugation, freeze-drying and grinding. Then mix the obtained CNC powder with 5% (w / w) citric acid (CA) and 1% (w / w) sodium hypophosphite (SHP). In the mixed solution, CNC:CA:H2O:SHP = 5:15:285:2.88 (w / w). After reacting for 1 hour, transfer it to a vacuum oven at 25 °C and 0.6 bar for 2 hours, take it out and let it stand at room temperature for 12 hours. Then heat the suspension at 60 °C for 48 hours to remove moisture, and then heat at 130 °C for 4 hours. Finally, obtain the CCNC powder through washing, centrifugation, dialysis purification and freeze-drying and grinding.

[0052] Dissolve 0.97 g of PLA particles in 9 mL of chloroform, stir at room temperature until dissolved, add 0.03 g of CCNC, ultrasonically treat and stir for 24 h, and finally use the solution casting method to prepare the composite film. After fully drying at room temperature in a fume hood, the PLA-CCNC composite film can be obtained.

[0053] Cover the PLA-CCNC composite film in the plant planting pots. Take samples every 15 days, ultrasonically clean them, and mark them as PED samples after natural drying. Place the PLA-CCNC composite film in a multi-lamp accelerated aging chamber (UVA-340, incident energy 1.1 W·m-2), and conduct ultraviolet irradiation at 35 °C. Take samples every 72 hours and mark them as UID samples.

[0054] Use an ultraviolet-visible spectrophotometer to measure the light transmittance coefficient of the film at a wavelength of 800 nm. Each sample is tested 5 times, and the average value is taken to reduce errors.

[0055] Use the p-norm algorithm to analyze the correlation between the light transmittance coefficient and the degradation days (PED and UID). By fitting the non-linear relationship curve between the light transmittance coefficient and the UID days, establish the prediction model F1. According to the F1 model, calculate the hypothetical UID days, combine the PED days to fit the linear relationship curve F2, and accurately estimate the service life of the PLA-CCNC composite film in the outdoor planting environment through the F2 model.

[0056] Example 4:

[0057] The method for establishing a service life prediction model of a PLA-based composite mulch film based on the p-norm algorithm in this example includes:

[0058] Mix 1 g of MCC with 60 mL of 6 mol / L HCl solution, ultrasonically treat for 10 minutes and stir and react at 90 °C for 4 hours. After cooling, wash with water until neutral, and obtain CNC through centrifugation, freeze-drying and grinding. Then mix the obtained CNC powder with 5% (w / w) citric acid (CA) and 1% (w / w) sodium hypophosphite (SHP). In the mixed solution, CNC:CA:H2O:SHP = 5:15:285:2.88 (w / w). After reacting for 1 hour, transfer it to a vacuum oven at 25 °C and 0.6 bar for 2 hours, take it out and let it stand at room temperature for 12 hours. Then heat the suspension at 60 °C for 48 hours to remove moisture, and then heat at 130 °C for 4 hours. Finally, obtain the CCNC powder through washing, centrifugation, dialysis purification and freeze-drying and grinding.

[0059] Dissolve 0.97 g of PLA particles with 9 mL of chloroform, stir at room temperature until dissolved, add 0.03 g of CCNC, ultrasonically treat and stir for 24 h. Finally, use the solution casting method to prepare the composite film, and fully dry it at room temperature in a fume hood to obtain the PLA-CCNC composite film.

[0060] Cover the plant planting pots with the PLA-CCNC composite film. Take samples every 15 days, ultrasonically clean them, and mark them as PED samples after natural drying. Place the PLA-CCNC composite film in a multi-lamp accelerated aging chamber (UVA-340, incident energy 1.1 W·m-2), and conduct ultraviolet irradiation at 35°C. Take samples every 72 hours and mark them as UID samples.

[0061] Use a UV-visible spectrophotometer (U-2900, Hitachi) to measure the reflectance coefficient of the film at a wavelength of 800 nm. Each sample is tested 5 times, and the average value is taken to reduce errors.

[0062] Use the p-norm algorithm to analyze the correlation between the reflectance coefficient and the degradation days (PED and UID). By fitting the non-linear relationship curve between the reflectance coefficient and the UID days, establish the prediction model F1. According to the F1 model, calculate the hypothetical UID days, combine the PED days to fit the linear relationship curve F2, and accurately estimate the service life of the PLA-CCNC composite film in the outdoor planting environment through the F2 model.

[0063] Comparative Example 1:

[0064] The method for establishing a prediction model for the service life of a PLA-based composite mulch film based on the linear regression equation algorithm in this comparative example includes:

[0065] Mix 1 g of MCC with 60 mL of 6 mol / L HCl solution, ultrasonically treat for 10 minutes and stir at 90°C for 4 hours. After cooling, wash with water until neutral, and obtain CNC through centrifugation, freeze-drying, and grinding. Then mix the obtained CNC powder with 5% (w / w) citric acid (CA) and 1% (w / w) sodium hypophosphite (SHP). In the mixture, CNC:CA:H2O:SHP = 5:15:285:2.88 (w / w). After reacting for 1 hour, transfer it to a vacuum oven at 25°C and 0.6 bar for 2 hours, take it out and let it stand at room temperature for 12 hours. Then heat the suspension at 60°C for 48 hours to remove moisture, and then heat it at 130°C for 4 hours. Finally, obtain the CCNC powder through washing, centrifugation, dialysis purification, and freeze-drying and grinding.

[0066] Dissolve 0.97 g of PLA particles in 9 mL of chloroform, stir at room temperature until dissolved, add 0.03 g of CCNC, ultrasonically treat and stir for 24 h, and finally use the solution casting method to prepare the composite film. After fully drying at room temperature in a fume hood, obtain the PLA-CCNC composite film.

[0067] Cover the plant planting pots with the PLA-CCNC composite film. Take samples every 15 days, ultrasonically clean them, and mark them as PED samples after natural drying. Place the PLA-CCNC composite film in a multi-lamp accelerated aging chamber (UVA-340, incident energy 1.1 W·m-2), and conduct ultraviolet irradiation at 35 °C. Take samples every 72 hours and mark them as UID samples.

[0068] Use a universal testing machine (Instron 5943) to test the tensile strength and elongation at break of the film at room temperature. Use a UV-visible spectrophotometer (U-2900, Hitachi) to measure the light transmittance coefficient and reflectance coefficient of the film at a wavelength of 800 nm. Each sample is tested 5 times, and the average value is taken to reduce errors.

[0069] By fitting the linear relationship curves of the four properties with the UID days respectively, establish the prediction model F1. According to the F1 model, calculate the hypothetical UID days, fit the linear relationship curve F2 combined with the PED days, and estimate the service life of the PLA-CCNC composite film in the outdoor planting environment through the F2 model.

[0070] Comparative Example 2:

[0071] The method for establishing the service life prediction model of the pure PLA mulch film based on the p-norm algorithm in this comparative example includes:

[0072] Dissolve 1 g of PLA particles in 9 mL of chloroform, stir at room temperature for 24 h, and finally prepare the film by the solution casting method. After fully drying at room temperature in a fume hood, the pure PLA film can be obtained.

[0073] Cover the plant planting pots with the pure PLA film. Take samples every 15 days, ultrasonically clean them, and mark them as PED samples after natural drying. Place the pure PLA film in a multi-lamp accelerated aging chamber (UVA-340, incident energy 1.1 W·m-2), and conduct ultraviolet irradiation at 35 °C. Take samples every 72 hours and mark them as UID samples.

[0074] Use a universal testing machine (Instron 5943) to test the tensile strength and elongation at break of the film at room temperature. Use a UV-visible spectrophotometer (U-2900, Hitachi) to measure the light transmittance coefficient and reflectance coefficient of the film at a wavelength of 800 nm. Each sample is tested 5 times, and the average value is taken to reduce errors.

[0075] Use the p-norm algorithm to analyze the correlation between the reflectance coefficient and the degradation days (PED and UID). By fitting the non-linear relationship curve between the reflectance coefficient and the UID days, establish the prediction model F1. According to the F1 model, calculate the hypothetical UID days, fit the linear relationship curve F2 combined with the PED days, and estimate the service life of the pure PLA film in the outdoor planting environment through the F2 model.

[0076] Analyze the results of Examples 1-4 and Comparative Examples 1-2:

[0077] Figure 1 This is a graph showing the change in the quality of environmental degradation of the PLA-CCNC composite films of Examples 1-4 and Comparative Example 1 of the present invention, and the pure PLA film of Comparative Example 2. As Figure 1 shown, compared with the pure PLA mulch film, the degradation rate of the PLA-CCNC composite mulch film with 3 wt% CCNC added is significantly accelerated in the ground planting environment, and at the same time, the controllability of degradation is also significantly improved. This result indicates that the addition of CCNC effectively optimizes the degradation behavior of PLA, making its degradation process in the natural environment more efficient and controllable.

[0078] Figure 2 This is a graph of the results of the service life prediction model of the PLA-based composite mulch film based on the p-norm algorithm in Examples 1-4 of the present invention. As Figure 2 shown, in the service life prediction model of the PLA-based composite mulch film based on the p-norm algorithm, the prediction curve of the tensile strength shows significant advantages, and its coefficient of determination is generally higher than other performance indicators. Finally, the prediction error rate is stably controlled within 3.36%. This result benefits from the core advantages of the p-norm algorithm: by dynamically aligning the degradation sequences at different time scales, effectively eliminating the interference caused by time shift, and accurately capturing the non-linear dynamic characteristics of material degradation. Tensile strength is a key indicator characterizing the mechanical properties of materials, and its degradation process is dominated by the chain scission mechanism induced by ultraviolet rays. The p-norm algorithm can more sensitively reflect the performance attenuation law under this mechanism by minimizing the path distance to quantify the degradation correlation, thus significantly improving the prediction accuracy.

[0079] Figure 3 This is a graph of the results of the service life prediction model of the PLA-based composite mulch film based on the linear regression equation algorithm in Comparative Example 1 of the present invention. As Figure 3 shown, in the prediction results using the traditional linear regression model, although the tensile strength is still the optimal indicator (R 2 = 0.842), its error rate (≤8.73%) is significantly lower than that of the p-norm model. This is because linear regression only relies on the simple linear relationship assumption and cannot handle the non-steady response introduced by environmental variables during the degradation process. In contrast, the p-norm model realizes the step-by-step analysis of complex degradation paths through a two-step regression strategy (first non-linearly fitting the UID data and then linearly mapping it to the PED scenario), verifying its technological breakthrough in dynamic environmental adaptability.

[0080] Figure 4 This is a graph of the results of the service life prediction model of the pure PLA mulch film based on the p-norm algorithm in Comparative Example 2 of the present invention. As Figure 4As shown, in the p-norm prediction model of pure PLA mulch film, the light transmittance coefficient has been proven to be the most reliable prediction index, with an error of no more than 4.14%. This result reveals the key role of material modification. Specifically, due to the loose arrangement of molecular chains in unmodified PLA, the uneven distribution of crystalline and amorphous regions leads to significant non-uniformity and uncontrollability in molecular chain breakage, resulting in a large uncertainty in the change of tensile strength. In contrast, the interfacial enhancement effect of CCNC can guide the molecular chains to break along an ordered path by regulating ultraviolet absorption and hydrolysis rate, thus significantly reducing the non-uniformity during the degradation process. This mechanism not only improves the controllability of material degradation but also provides an important theoretical basis for the further modification of PLA.

[0081] Given the numerous embodiments of the present invention, each embodiment can be determined according to actual application requirements within the defined range of each parameter. The experimental data is vast and numerous, not suitable for listing and explaining one by one here. However, the content to be verified and the final conclusions obtained in each embodiment are similar.

[0082] The above description only details the preferred embodiments and principles of the present invention. For those of ordinary skill in the art, based on the idea provided by the present invention, there will be changes in the specific implementation manners, and these changes should also be regarded as the protection scope of the present invention.

Claims

1. A method for predicting the service life of PLA-based composite mulch based on p-norm algorithm, characterized in that: The following steps are involved: (1) Using the p-norm algorithm, the time series data of the performance indicators of polylactic acid composites under ultraviolet irradiation degradation (UID) and planting environment degradation (PED) conditions were analyzed to evaluate their similarities; (2) Based on the similarity analysis in step (1), a correlation model between UID days and PED days was established through a two-step method, specifically: (i) Fitting the nonlinear regression curve of UID treatment days (x1) and performance index change (y1) to obtain the first function (F1); (ii) Based on F1, the assumed UID days (x2) are inferred, and the linear regression curve of PED days (x3) and assumed UID days (x2) is combined to fit the second function (F2), and finally the predicted PED days (x4) are calculated through F2.

2. The method according to claim 1, characterized in that The performance indicators include elongation at break, tensile strength, light transmittance and reflectance.

3. The method according to claim 1, characterized in that The UID experimental conditions include: ultraviolet wavelength of UVA-340, irradiation energy of 0.5-1.5 W·m-2, temperature of 15-40°C, each cycle lasts 1-3 days, and the total number of experimental cycles is 10-15 times.

4. The method according to claim 1, characterized in that: The polylactic acid composite material is a PLA-CCNC composite film, and its preparation method comprises: after cellulose nanocrystals (CNC) are modified by citric acid grafting, they are blended with polylactic acid (PLA) at a mass ratio of 3%, and a film is prepared by a solution casting method.

5. The method according to claim 1, characterized in that The data collection interval of the performance indicators is once every 10-15 days, and the specification of the soil covering film in the PED experiment is 5 cm×5 cm.

6. The method according to claim 1, characterized in that The p-norm algorithm is used to align degradation data at different time scales and calculate the 2-norm of the degradation process of UID and PED to determine their correlation.

7. The method according to claim 1, characterized in that The life prediction method further includes a pretreatment step for the polylactic acid composite material sample, including ultrasonic cleaning, deionized water rinsing and room temperature drying.

8. A prediction system based on the method according to any one of claims 1 to 7, characterized in that: include: (i) Data acquisition module, used to obtain performance indicator data in UID and PED experiments; (ii) p-norm algorithm processing module, used to analyze the similarity of time series data; (iii) Model building module, which generates F1 and F2 functions and outputs the predicted PED days.

9. The prediction system according to claim 8, characterized in that The system is integrated into a computer or cloud platform to support real-time data input and dynamic model updating.

10. A storage medium, characterized in that: The method comprises computer executable instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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